Screening a novel signature and predicting the immune landscape of metastatic osteosarcoma in children via immune-related lncRNAs

Identification and prognostic evaluation of differentially expressed long noncoding RNAs associated with immune infiltration in osteosarcoma

Osteosarcoma is a malignant bone cancer that originates from the bone with the strongest invasiveness. Tumor formation strongly correlates with immune cell infiltration into the tumor immune microenvironment (TIME). Therefore, we aimed to identify TIME-related biomarkers as potential prognostic markers of osteosarcoma. The mRNA and long noncoding RNA (lncRNA) transcriptome data of 88 patients with osteosarcoma and the expression profile of GSE99671 were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus, respectively. Immune infiltration scores and types were evaluated using ESTIMATE and CIBERSORT. A linear model was established to identify the differentially expressed genes (DEGs) and lncRNAs (DElncRNAs). Functional enrichment analysis of DEGs was conducted by Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, gene set enrichment analysis, and gene set variation analysis. DElncRNAs were analyzed using a weighted gene co-expression network. Least absolute shrinkage and selection operator regression was applied to screen for prognostic markers. Patient survival was predicted by the risk score and analyzed by receiver operating characteristic curve. Clinical features affecting patient survival were assessed. Immune infiltration positively correlated with osteosarcoma patient survival. Different immune cell infiltrates in patients with osteosarcma may serve as prognostic indicators and targets for immunotherapy. In total, 1125 DEGs, 80 DElncRNAs, and 11 pairs of co-expressed lncRNA-mRNAs were identified. DEGs in the three modules were associated with immune infiltration into the TIME. Four DElncRNAs, namely AC015819.1, AC015911.3, AL365361.1, and USP30-AS1, showed good prognostic ability for osteosarcoma and were positively correlated with the immune score. Tumor metastasis and risk scores alone were good prognostic indicators, and a combination of the two variables can better predict the prognosis of osteosarcoma. We identified four lncRNAs, AC015819.1, AC015911.3, AL365361.1, and USP30-AS1, as potential biomarkers for osteosarcoma prognosis.

Decoding the Impact of Tumor Microenvironment in Osteosarcoma Progression and Metastasis

Osteosarcoma (OS) is a heterogeneous, highly metastatic bone malignancy in children and adolescents. Despite advancements in multimodal treatment strategies, the prognosis for patients with metastatic or recurrent disease has not improved significantly in the last four decades. OS is a highly heterogeneous tumor; its genetic background and the mechanism of oncogenesis are not well defined. Unfortunately, no effective molecular targeted therapy is currently available for this disease. Understanding osteosarcoma's tumor microenvironment (TME) has recently gained much interest among scientists hoping to provide valuable insights into tumor heterogeneity, progression, metastasis, and the identification of novel therapeutic avenues. Here, we review the current understanding of the TME of OS, including different cellular and noncellular components, their crosstalk with OS tumor cells, and their involvement in tumor progression and metastasis. We also highlight past/current clinical trials targeting the TME of OS for effective therapies and potential future therapeutic strategies with negligible adverse effects.

LncRNA VPS9D1-AS1 Sponging miR-520a-5p Contributes to the Development of Uterine Corpus Endometrial Carcinoma by Enhancing BIRC5 Expression

The pattern of VPS9D1-AS1 expression and its effects on uterine corpus endometrial carcinoma (UCEC) remained unclear. VPS9D1-AS1, miR-520a-5p, and BIRC5 mRNA levels were quantified by qRT-PCR. Bax, Bcl-2, N-cadherin, E-cadherin, and BIRC5 protein levels were analyzed through western blotting. Cell migration, invasion, proliferation, as well as apoptosis of cells were checked after performing assay for wound-healing, Transwell, cell-counting kit-8 (CCK-8) assay, and western blotting. VPS9D1-AS1 effects on UCEC were observed in nude mice. Through bioinformatics tools, we analyzed the association present among miR-520a-5p, BIRC5, and VPS9D1-AS1 along with RNA immunoprecipitation, and Dual-Luciferase verification reporter analysis. Our findings suggested VPS9D1-AS1 gene expression was up-regulated in both tissues as well as cells of UCEC. VPS9D1-AS1 knockdown suppressed migration, invasion, epithelial-mesenchymal transition (EMT) along with proliferation of UCEC cells, caused in vitro cell apoptosis initiation, and in vivo reduction of tumor growth. Mechanistically, it was verified that VPS9D1-AS1 targeted BIRC5 and caused miR-520a-5p sponging. Inhibitor of miR-520-5p markedly reversed the anti-tumor effects of VPS9D1-AS1 knockdown or BIRC5 knockdown on UCEC progression. Our studies revealed that VPS9D1-AS1 contributed to the UCEC development and progression by binding to miR-520a-5p competitively and inducing BIRC5 expression, indicating that VPS9D1-AS1 might act as a therapeutic target to develop new therapies for UCEC patients.

Immune Microenvironment in Osteosarcoma: Components, Therapeutic Strategies and Clinical Applications

Osteosarcoma is a primary malignant tumor that tends to threaten children and adolescents, and the 5-year event-free survival rate has not improved significantly in the past three decades, bringing grief and economic burden to patients and society. To date, the genetic background and oncogenesis mechanisms of osteosarcoma remain unclear, impeding further research. The tumor immune microenvironment has become a recent research hot spot, providing novel but valuable insight into tumor heterogeneity and multifaceted mechanisms of tumor progression and metastasis. However, the immune microenvironment in osteosarcoma has been vigorously discussed, and the landscape of immune and non-immune component infiltration has been intensively investigated. Here, we summarize the current knowledge of the classification, features, and functions of the main infiltrating cells, complement system, and exosomes in the osteosarcoma immune microenvironment. In each section, we also highlight the complex crosstalk network among them and the corresponding potential therapeutic strategies and clinical applications to deepen our understanding of osteosarcoma and provide a reference for imminent effective therapies with reduced adverse effects.

Prognostic Value of Drug Targets Predicted Using Deep Bioinformatic Analysis of m6A-Associated lncRNA-Based Pancreatic Cancer Model Characteristics and Its Tumour Microenvironment

The role of N6-methyladenosine (m6A)-associated long-stranded non-coding RNA (lncRNA) in pancreatic cancer is unclear. Therefore, we analysed the characteristics and tumour microenvironment in pancreatic cancer and determined the value of m6A-related lncRNAs for prognosis and drug target prediction. An m6A-lncRNA co-expression network was constructed using The Cancer Genome Atlas database to screen m6A-related lncRNAs. Prognosis-related lncRNAs were screened using univariate Cox regression; patients were divided into high- and low-risk groups and randomised into training and test groups. In the training group, least absolute shrinkage and selection operator (LASSO) was used for regression analysis and to construct a prognostic model, which was validated in the test group. Tumor mutational burden (TMB), immune evasion, and immune function of risk genes were analysed using R; drug sensitivity and potential drugs were examined using the Genomics of Drug Sensitivity in Cancer database. We screened 129 m6A-related lncRNAs; 17 prognosis-related m6A-related lncRNAs were obtained using multivariate analysis and three m6A-related lncRNAs (AC092171.5, MEG9, and AC002091.1) were screened using LASSO regression. Survival rates were significantly higher (p < 0.05) in the low-risk than in the high-risk group. Risk score was an independent predictor affecting survival (p < 0.001), with the highest risk score being obtained by calculating the c-index. The TMB significantly differed between the high- and low-risk groups (p < 0.05). In the high- and low-risk groups, mutations were detected in 61 of 70 samples and 49 of 71 samples, respectively, with KRAS, TP53, and SMAD4 showing the highest mutation frequencies in both groups. A lower survival rate was observed in patients with a high versus low TMB. Immune function HLA, Cytolytic activity, and Inflammation-promoting, T cell co-inhibition, Check-point, and T cell co-stimulation significantly differed in different subgroups (p < 0.05). Immune evasion scores were significantly higher in the high-risk group than in the low-risk group. Eight sensitive drugs were screened: ABT.888, ATRA, AP.24534, AG.014699, ABT.263, axitinib, A.443654, and A.770041. We screened m6A-related lncRNAs using bioinformatics, constructed a prognosis-related model, explored TMB and immune function differences in pancreatic cancer, and identified potential therapeutic agents, providing a foundation for further studies of pancreatic cancer diagnosis and treatment.